An entity, such as an insurance company, may want to analyze or “mine” large amounts of data, such as text data. For example, an insurance company might want to analyze tens of thousands of insurance claim text files to look for patterns (e.g., a particular type of injury has occurred more frequently for employees who work in a particular industry). Note that an entity might analyze this data in correction with different types of applications, and, moreover, different applications may need to analyze the data differently. For example, the term “IV” might referent to an “Insured Vehicle” when it appears in an automobile accident report and to “Intra-Venous” when it appears in a medical file. It can be difficult to identify patterns across such large amounts of data and different types of applications. In addition, manually managing the different needs and requirements (e.g., different business logic rules) associated with different applications can be a time consuming and error prone process. As a result, it would be desirable to provide systems and methods for efficiently and accurately mining text data.